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1. STT 430/530, Nonparametric StatisticsTR 9:30-10:45 BR 201DExam: Tuesday December 11, 2007 8-11 am • Text: • Introduction to Modern Nonparametric Statistics, J.J. Higgins, Duxbury Press (2004) (required) • We’ll proceed in a logical way to look at parametric vs. nonparametric methods for: • one-sample problems • two-sample problems • k-sample problems, k>2 • Standard nonparametric techniques include rank-based methods, permutation tests, and others… • But we’ll also consider two more modern techniques: the bootstrap and smoothing methods for density estimation. • Most of these methods are highly computationally intensive - and we’ll be using the open source programming language called R to do our computations (no previous experience required)

2. For some computations we’ll also use SAS • We won’t do much theory (i.e., mathematical statistics) but won’t shy away from the notation, logic, outlines of proofs, etc., when needed • Emphasis on doing the analyses and interpreting the results - writing is important • There will be homework problems, a midterm exam and a comprehensive final exam -- the exams will involve take-home components. I’ll probably weight the three parts equally to determine your course grade. • For next Tuesday: Read Chapter 0 in the Higgins book - we’ll review some ideas from your earlier statistics courses before jumping into nonparametrics.